An enterprise may run numerous applications that rely on data received from one or more sources. The application may pull the data from specifically allocated portions of a queue, and the portions of memory may vary in size according to the application and the enterprise's resources. More complex applications may analyze vast amounts of data, requiring evermore memory to hold the data for processing. The amount of data analyzed by an application and the rate at which the application analyzes the stored data may change over time, making it difficult to efficiently allocate memory for the received data. If a system allocates an insufficient amount of memory to store the incoming data, the queue may become full which may lead to queue overflow or data corruption when subsequent data is received.
Preventing queue overflow while maintaining data integrity presents a number of technical challenges. Systems may have a limited allocation of memory to hold and process data. As the queue storage reaches capacity, attempts to add to the queue may lead to queue overflow and/or data corruption. Applications that utilize the data stored in the queue may encounter errors or stop functioning in response to the queue overflow. Conventional ways of preventing memory overflow require clearing a queue manually; resetting the queue (which causes a loss of useful data); or expanding the queue allocation, which can be costly and resource intensive.